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              基于自適應全局定位算法的帶鋼表面缺陷檢測

              王延舒 余建波

              王延舒, 余建波. 基于自適應全局定位算法的帶鋼表面缺陷檢測. 自動化學報, 2023, 45(x): 1?16 doi: 10.16383/j.aas.c210467
              引用本文: 王延舒, 余建波. 基于自適應全局定位算法的帶鋼表面缺陷檢測. 自動化學報, 2023, 45(x): 1?16 doi: 10.16383/j.aas.c210467
              Wang Yan-shu, Yu Jian-Bo. Strip surface defect detection based on adaptive global localization algorithm. Acta Automatica Sinica, 2023, 45(x): 1?16 doi: 10.16383/j.aas.c210467
              Citation: Wang Yan-shu, Yu Jian-Bo. Strip surface defect detection based on adaptive global localization algorithm. Acta Automatica Sinica, 2023, 45(x): 1?16 doi: 10.16383/j.aas.c210467

              基于自適應全局定位算法的帶鋼表面缺陷檢測

              doi: 10.16383/j.aas.c210467
              基金項目: 國家自然科學基金(No. 71777173),上??莆翱萍紕撔滦袆佑媱潯备咝录夹g領域項目(No. 19511106303),中央高?;緲I務經費項目資助
              詳細信息
                作者簡介:

                王延舒:同濟大學機械與能源工程學院工業工程專業研究生. 2020年獲四川大學工業工程專業學士學位. 主要研究方向為機器學習, 深度學習, 視覺檢測與識別. E-mail: 2030211@#edu.cn

                余建波:同濟大學機械與能源工程學院教授. 2009年獲上海交通大學機械工程學院博士學位. 主要研究方向為:機器學習, 深度學習, 智能質量管控, 過程控制, 視覺檢測與識別.本文通信作者. E-mail: jbyu@#edu.cn

              Strip Surface Defect Detection Based on Adaptive Global localization Algorithm

              Funds: Supported by National Natural Science Foundation of China (71777173), “Action plan for scientific and technological innovation” of Shanghai Science and Technology Commission (19511106303), Fundamental Research Funds for the Central Universities
              More Information
                Author Bio:

                WANG Yan-Shu Postgraduate candidate at the Mechanical and Energy Engineering, Tongji University. He received his bachelor degree from Sichuan University. His research interests include Machine learning and visual detection and recognition

                YU Jian-Bo Professor at the Mechanical and Energy Engineering, Tongji University. He received his Ph. D. degree from Shanghai Jiaotong University. His research interests include Machine learning, Deep learning, intelligent quality control, Process control, visual inspection and identification.Corresponding author of this paper

              • 摘要: 針對熱軋帶鋼表面缺陷檢測存在的智能化水平低、檢測精度低和檢測速度慢等問題, 本文提出了一種基于自適應全局定位網絡(Adaptive global localization network, AGLNet)的深度學習缺陷檢測算法. 首先, 引入了一種殘差網絡(Residual network, ResNet)與特征金字塔網絡(Feature pyramid network, FPN)集成的特征提取結構, 減少缺陷語義信息在層級傳遞間的消失; 其次, 提出基于Tree-structure parzen estimation的自適應樹型候選框提取網絡(Adaptive tree-structure region proposal network, AT-RPN), 無需先驗知識的測試積累, 避免了人為調參的訓練模; 最后, 引入了全局定位算法(Global localization regression)算法以全局定位的模式在復雜的缺陷檢測中實現缺陷更精確定位.本文實現一種快速、準確、更智能化、更適用于實際工業應用的熱軋帶鋼表面缺陷的算法.實驗結果表明, AGLNet在NEU-DET熱軋帶鋼表面缺陷數據集上的檢測速度保持在11.8fps, 平均精度達到了79.90 %, 優于目前其他深度學習帶鋼表面缺陷檢測算法; 另外該算法還具備較強的泛化能力.
              • 圖  1  AGLNet網絡

                Fig.  1  The structure of AGLNet

                圖  2  TPE自適應anchor-ratio調節模塊流程圖

                Fig.  2  Flow chart of TPE adaptive anchor ratio adjustment module

                圖  3  AT-RPN整體結構圖

                Fig.  3  Whole structure of AT-RPN

                圖  4  AGLNet與Faster R-CNN和Grid R-CNN的比較

                Fig.  4  Comparison of AGLnet with Fast R-CNN and Grid R-CNN

                圖  5  NEU-DET數據集熱軋帶鋼表面缺陷

                Fig.  5  Surface defects of hot rolled strip in NET-DET dataset

                圖  6  AT-RPN, RPN和AABO的分類損失函數變化對比

                Fig.  6  The change of classification loss function of AT-RPN, RPN and AABO

                圖  7  AT-RPN, RPN和AABO的的位置回歸損失函數變化對比

                Fig.  7  The change of bounding box regression loss function of AT-RPN, RPN and AABO

                圖  8  PCB-Master數據集中的高寬比統計結果

                Fig.  8  Statistical results of aspect ratio in PCB master dataset

                圖  9  PCB-Master檢測結果

                Fig.  9  PCB master test results

                圖  10  AGLNet模型下裂紋和壓入氧化缺陷檢測結果與人工標注位置對

                Fig.  10  Comparison between inspection results of Crazing and Rolled-in_scale defects under AGLNet model and manually marked positions

                表  1  AGLNet、Gird R-CNN and Faster R-CNN基于NEU-DET數據集的對比測試結果

                Table  1  Comparison results of AGLNet, Gird R-CNN and Fast R-CNN based on NEU-DET dataset

                裂紋 夾雜 斑塊 麻點 壓入氧化 劃痕
                AGLNet
                Grid R-CNN
                Faster R-CNN
                下載: 導出CSV

                表  2  各個模型在NEU-DET數據集的缺陷檢測平均精度結果(%)

                Table  2  Average accuracy results of defect detection on NEU-DET dataset of comparative experiment (%)

                方法 平均精度均值 裂紋 夾雜 斑塊 麻點 壓入氧化 劃痕
                Faster R-CNN 79.20 71.31 84.63 82.92 80.17 80.31 75.87
                RetinaNet 75.36 53.02 78.74 93.33 91.37 62.21 73.49
                FCOS 75.18 52.41 75.03 91.48 84.85 62.86 84.43
                Grid R-CNN 73.14 41.52 78.68 86.23 86.47 59.74 86.21
                YOLO-v1 62.90 42.35 63.42 68.23 66.49 69.37 67.53
                YOLO-v2 66.53 47.35 70.47 72.23 65.82 65.49 77.84
                YOLO-v3 69.40 68.39 61.88 71.44 68.33 72.66 73.71
                YOLO-v4 77.99 64.87 70.84 93.24 83.83 69.52 85.63
                YOLO-v5 76.82 62.42 75.76 84.23 81.27 64.59 92.63
                YOLOF 77.32 63.48 71.82 90.56 85.21 64.24 88.63
                AGLNet 79.90 54.72 83.31 88.63 91.67 64.42 96.64
                下載: 導出CSV

                表  3  各模型FLOPs, Params和FPS對比結果

                Table  3  Comparison of Flops, Params and FPS of each model

                方法 FLOPs Params Fps
                Faster R-CNN 408.36GMac 98.25 M $\sim$8.2
                RetinaNet 239.32GMac 37.74 M $\sim$12.3
                FCOS 438.68GMac 89.79 M $\sim$9.3
                Grid R-CNN 329.51GMac 64.32 M $\sim$10.2
                YOLO-v3 89.45GMac 27.84 M $\sim$15.4
                YOLOF 151.47GMac 63.24M $\sim$13.4
                AGLNet 273.95GMac 79.8 M $\sim$11.8
                下載: 導出CSV

                表  4  各類缺陷在不同IoU閾值下的測試結果

                Table  4  Detection results of various defects under different IOU thresholds

                IoU閾值 缺陷類型 gts Dets Recall mAP
                IoU0.5 裂紋 139 1 886 0.935 54.72
                IoU0.75 裂紋 139 1 823 0.923 47.48
                IoU0.5 夾雜 181 1 188 0.945 83.31
                IoU0.75 夾雜 181 1 163 0.932 82.17
                IoU0.5 斑塊 151 627 0.960 88.63
                IoU0.75 斑塊 151 591 0.942 89.45
                IoU0.5 麻點 88 689 0.955 91.67
                IoU0.75 麻點 88 636 0.938 89.24
                IoU0.5 壓入氧化 126 1 034 0.893 64.42
                IoU0.75 壓入氧化 126 1 051 0.882 59.66
                IoU0.5 劃痕 117 317 0.991 96.64
                IoU0.75 劃痕 117 322 0.986 92.79
                IoU0.5 全部缺陷 802 5 741 0.947 79.90
                IoU0.75 全部缺陷 802 5 586 0.934 76.79
                下載: 導出CSV

                表  5  消融實驗結果

                Table  5  Ablation results

                序號 ResNet50+FPN ResNet50 AT-RPN RPN mAP(%) fps GPU Memory Usage
                1 79.90 11.8 5568MiB
                2 78.64 10.3 7039MiB
                3 77.97 12.2 5024MiB
                4 76.82 10.6 6436MiB
                下載: 導出CSV

                表  6  消融實驗對比結果

                Table  6  Comparison results of ablation experiments

                序號 對比試驗 mAP提升 fps提升 節約顯存占用率
                1 實驗1*實驗2 1.26% 1.5 20.89%
                2 實驗3*實驗4 1.15% 1.6 21.93%
                3 實驗1*實驗3 1.93% -0.4 -10.82%
                4 實驗2*實驗4 1.82% -0.3 -9.36%
                5 實驗1*實驗4 3.08% 1.2 13.49%
                下載: 導出CSV

                表  7  PCB-Master數據集基本信息

                Table  7  Basic information of PCB master dataset

                缺陷類型 圖像數量 缺陷數量
                漏孔(Missing_hole) 115 497
                鼠咬(Mouse_bite) 115 492
                斷路(Open_circuit) 115 482
                短路(Short) 115 491
                毛刺(Spur) 115 488
                下載: 導出CSV

                表  8  各個模型在PCB-Master數據集上測試結果(%)

                Table  8  Test results of each model on PCB master dataset (%)

                Faster R-CNN RetinaNet FCOS Grid R-CNN Yolo-v3 YOLOF AGLNet
                MAP(%) 86.9 91.16 88.9 94.3 79.8 95.0 96.9
                漏孔 0.874 0.915 0.907 0.956 0.858 0.942 0.995
                鼠咬 0.849 0.905 0.852 0.934 0.793 0.934 0.952
                斷路 0.862 0.897 0.847 0.915 0.747 0.886 0.929
                短路 0.895 0.922 0.928 0.997 0.832 0.997 0.997
                毛刺 0.869 0.953 0.915 0.954 0.826 0.989 0.997
                余銅 0.865 0.875 0.880 0.905 0.731 0.954 0.942
                下載: 導出CSV

                表  9  PCB-Master測試集檢測數據統計

                Table  9  Data statistics of PCB-Master defect detection test set

                缺陷類別 gts Dets recall ap
                漏孔(Missing_hole) 169 696 0.998 0.995
                鼠咬(Mouse_bite) 142 665 0.990 0.952
                斷路(Open_circuit) 142 667 0.990 0.929
                短路(Short) 132 590 1 0.997
                毛刺(Spur) 143 687 1 0.997
                余銅(Spurious_copper) 137 644 0.979 0.942
                全部缺陷總計 865 3949
                下載: 導出CSV
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                        • 收稿日期:  2021-07-23
                        • 錄用日期:  2021-11-26
                        • 網絡出版日期:  2023-02-06

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